federico

T3 · host / generalist

Leads research on Cursor's agentic coding model Composer 2, focusing on model training strategy, RL methodology, and specialization for software engineering within the Cursor environment.

2 calls·2 names·50% bull·last heard 3 months ago·Sequoia Capital
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1sthigh conviction
$CURSORCursorposition

Cursor builds specialized foundation model Composer 2 for 10x cost advantage over general coding models

Cursor argues that allocating all model weights to a single task (software engineering inside Cursor) enables order-of-magnitude lower inference cost vs general models like Opus, while achieving superior performance through RL on their proprietary harness and user data.

2ndlow conviction
$MOONSHOT-AIMoonshot AI

Moonshot's Kimi 2.5 serves as strong sparse MoE base for Cursor's specialized coding model

Cursor selected Kimi 2.5 (1T parameter MoE with 30B active parameters) as the base model for Composer 2, leveraging its sparse architecture and strong code capabilities before applying continual pre-training and large-scale RL.

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2 total
$CURSOR
Cursor
HIGHfederico·Sequoia Capital·3 months ago· position
Cursor builds specialized foundation model Composer 2 for 10x cost advantage over general coding models
Cursor argues that allocating all model weights to a single task (software engineering inside Cursor) enables order-of-magnitude lower inference cost vs general models like Opus, while achieving superior performance through RL on their proprietary harness and user data.
"We care about only one task. We don't even care about coding or programming necessarily. We care about software engineering inside cursor and inside cursor only. And so, what if w…"
2:01
$MOONSHOT-AI
Moonshot AI
LOWfederico·Sequoia Capital·3 months ago
Moonshot's Kimi 2.5 serves as strong sparse MoE base for Cursor's specialized coding model
Cursor selected Kimi 2.5 (1T parameter MoE with 30B active parameters) as the base model for Composer 2, leveraging its sparse architecture and strong code capabilities before applying continual pre-training and large-scale RL.
"We started from a very strong base, which is uh Kimmy 2.5. It's like a 1 trillion and parameter MoE, that's 30 B active, so very very sparse, actually."
6:30